Source code for pycif.plugins.datastreams.fluxes.VPRM_nc.read

import datetime
import os

import numpy as np
import xarray as xr
from netCDF4 import Dataset

from .....utils.netcdf import readnc
from .....utils.hdf5 import _hdf5_lock
from logging import info, debug



def read(
        self,
        name,
        varnames,
        dates,
        files,
        interpol_flx=False,
        comp_type=None,
        tracer=None,
        **kwargs
):
    """Get fluxes from VPRM files and load them into a pyCIF variable.

    For each date/file pair, opens the file and reads the field at the
    hour-of-day index matching the requested date (each file is expected to
    hold 24 hourly time steps for one day).

    Args:
        name (str): Unused directly, kept for interface consistency with
            other flux plugins.
        varnames (str): Name of the variable to read in the file.
        dates (list): list of ``[start, end]`` date intervals to extract;
            only the start of each interval is used to select the hour of
            day.
        files (list): list of files matching `dates` (each a single-element
            list, per `fetch`'s output).
        interpol_flx (bool): Unused, kept for interface consistency.
        comp_type: Unused, kept for interface consistency.
        tracer: Unused directly, kept for interface consistency.

    Return:
        xr.DataArray: the flux data with dimensions
            ``(time, lev, lat, lon)``.
    """
    # list of the various fields read:
    data = []
    outdate = []
    for dd, ff in zip(dates, files):
        debug(f'Here put the reading of {[varnames]} in {ff} for {dd}')
        with _hdf5_lock:
            nc = xr.open_dataset(ff[0], decode_times=False)
            read_field = nc[varnames][dd[0].hour].values
        debug('e.g. get a 3d array read_field')
        data.append(read_field)
        outdate.append(dd[0])
    
    debug("check")
    debug(dates)
    debug(len(data[0]))
    debug(np.array(data).shape)

    # if only one level for emissions, create the axis:
    xmod = xr.DataArray(
        np.array(data)[:, np.newaxis, ...],
        coords={"time": outdate},
        dims=("time", "lev", "lat", "lon"),
    )

    return xmod